Load flow analysis by G-S reduction and restoration method

Hasan, Abdul-Razak Sh.; Krishnaparandhama, T.; Howaidi, A.M.

doi:10.1049/ip-c.1992.0055

Cited by 5 publications

(2 citation statements)

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“…For large networks, this is difficult, since the equations that describe the relations between the various parameters are nonlinear [1]. Although no analytical solution to these equations is known, various numerical methods exist, such as Newton-Raphson (NR), Gauss-Seidel (GS), fast decoupled power flow and fixed-point iterative method [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…These characteristics have been shown in the literature to produce ill-conditioned problems [6]. The most popular family of methods for solving the PF problem in radial distribution systems (RDSs) are numerical iterative methods, such as modifications of the NR and GS approaches or their derivatives, such as Fast Decoupled Load Flow [2][3][4][5]. All of these methods solve a set of 2(n − 1) nonlinear equations (where n is the number of nodes in the power grid).…”

Section: Introductionmentioning

confidence: 99%

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State solutions for distribution systems and switching event using a neural network

Yaniv

Lin

Raz

et al. 2021

IET Generation Trans & Dist

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Power flow calculations are an essential stage in many planning and control applications for distribution systems.To use these in control applications, however, the calculation time needs to be improved, and this can be done by the use of a trained ANN. This paper presents the considerations for constructing ANNs for DSs, and describes a method for training the system in order to support switching events representing a change in topology. The solutions for three DSs, balanced as well as unbalanced, are presented and the various considerations affecting the most appropriate ANN construction are discussed. The results are compared to the solution from the classical complex Newton-Raphson and the fixed-point iterative methods. The solutions have very high precision and good results are found for switched laterals. The computational performance is also compared and an improvement of two orders of magnitude is observed.

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Section: Introductionmentioning

confidence: 99%